Multi-resolution residual deep neural network for improving pelvic CBCT image quality.
Med Phys 2022;
49:1522-1534. [PMID:
35034367 DOI:
10.1002/mp.15460]
[Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 11/16/2021] [Accepted: 12/20/2021] [Indexed: 11/08/2022] Open
Abstract
PURPOSE
Cone-beam computed tomography (CBCT) is frequently used for accurate image guided radiation therapy (IGRT). However, the poor CBCT image quality prevents its further clinical use. Thus, it is important to improve the HU accuracy and structure preservation of CBCT images.
METHODS
In this study, we proposed a novel method to generate synthetic CT (sCT) images from CBCT images. A multi-resolution residual deep neural network (RDNN) was adopted for image regression from CBCT images to planning CT (pCT) images. At the coarse level, RDNN was first trained with a large amount of lower resolution images, which can make the network focus on coarse information and prevent overfitting problems. More fine information was obtained gradually by fine-tuning the coarse model using fewer number of higher resolution images. Our model was optimized by using aligned pCT and CBCT image pairs of a particular body region of 153 prostate cancer patients treated in our hospital (120 for training, 33 for testing). Five-fold cross-validation was used to tune the hyperparameters and the testing data were used to evaluate the performance of the final models.
RESULTS
The mean absolute error (MAE) between CBCT and pCT on the testing data was 352.56 HU, while the MAE between the sCT and pCT images was 52.18 HU for our proposed multi-resolution RDNN model, which reduced the MAE by 85.20% (p < 0.01). In addition, the average structural similarity index measure (SSIM) between the sCT and CBCT was 19.64% (p = 0.01) higher than that of pCT and CBCT.
CONCLUSIONS
The sCT images generated using our proposed multi-resolution RDNN have higher HU accuracy and structural fidelity, which may promote the further applications of CBCT images in the clinic for structure segmentation, dose calculation and adaptive radiotherapy planning. This article is protected by copyright. All rights reserved.
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